Incomplete Information and the Liquidity Premium Puzzle

• 成果类型: Article
• 署名作者: Chen, Yingshan; Dai, Min; Goncalves-Pinto, Luis; Xu, Jing; Yan, Cheng
• 署名单位: South China University of Technology; National University of Singapore; National University of Singapore; University of New South Wales Sydney; Chinese University of Hong Kong; Renmin University of China; University of Essex
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2020.3726
• 发表日期: 2021
• 页码: 5703-5729
• 关键词: Regime shifts; incomplete information; transaction costs; Liquidity premia

摘要

We examine the problem of an investor who trades in a market with unobservable regime shifts. The investor learns from past prices and is subject to transaction costs. Our model generates significantly larger liquidity premia compared with a benchmark model with observable market shifts. The larger premia are driven primarily by suboptimal risk exposure, as turnover is lower under incomplete information. In contrast, the benchmark model produces (mechanically) high turnover and heavy trading costs. We provide empirical support for the amplification effect of incomplete information on the relation between trading costs and future stock returns. We also show empirically that such amplification is not driven by turnover. Overall, our results can help explain the large disconnect between theory and evidence regarding the magnitude of liquidity premia, which has been a longstanding puzzle in the literature.